A Technology Model for Optimizing a Sustainable Steelmaking Process

Abstract

Primary steel production based on iron ore, and currently on fossil carbon carriers, is highly greenhouse gas (GHG) emission intensive. Nevertheless, the dominant production route is very energy efficient. Relevant progress in terms of GHG emissions, compatible with a low-carbon economy, is impossible with the current production route. A promising alternative is the so-called direct-reduction route, where iron ore is reduced in with natural gas and / or hydrogen. Technologically, this production route is already mature. But, it is not yet economically competitive with blast-furnace based steelmaking. Energy costs are a key driver of production costs for the direct-reduction based production route. Then again, this route offers some degrees of technological flexibility. Both natural gas and hydrogen can be used as reducing gases, allowing flexibility. The solid intermediate product, direct reduced iron (DRI), can be stored, and the same is true for hydrogen, which enables load flexibility. Given the difficult competitiveness of this steelmaking process, the central idea of this thesis was to test whether these degrees of flexibility could be used to reduce production costs. Moreover, these flexibilities might even lead to higher emission reductions. To test to which degree technological flexibilities can improve competitiveness by reducing production costs and even lead to lower specific GHG emissions, a technology-focused linear programming model was developed, and implemented with a Python code. This model reflects the key technology components of a direct-reduction steelmaking route, considers different technological restrictions, aims at production costs minimization, and emphasizes GHG emissions reduction as a key constraint. Different scenarios, reflecting the developments of energy system boundary conditions, as well as different technology design variations with respect to the technological flexibilities of the process, were designed and corresponding model runs carried out. Generally, results can be expected to differ according to the price and demand parameters given exogenously. Therefore, the modelling had to be done for a concrete energy system. Here, Germany was chosen as a case study. The modelling results clearly show that using all technological flexibility options (denoted as “Full Flexibility” design variation) offers benefits regarding production costs as well as GHG emissions reduction, compared to the case where none of these flexibility options (denoted as “No Flexibility”) are available for the optimization. For the 2020 and 2030 scenarios, natural gas is key for higher emission reductions. For the 2040 and 2050 scenarios, flexibility options show lesser impact on maximum GHG reductions achievable, but relevant production cost reductions. Specific production costs, per ton of crude steel produced and ton of CO2 emissions mitigated, are 18 % higher for “No Flexibility” compared to “Full Flexibility” in 2040, and 15 % higher in 2050. From these results the clear conclusion can be drawn that the assessed technological flexibility options reduce production costs, enhance economic competitiveness, enable higher maximum GHG emission reductions, and lower GHG abatement costs. The model developed and the results achieved could be very helpful both for both steel corporations and policy makers. Steel producers might use this model to implement and test their own production strategy. Policy makers might use the model, for instance, to quantify the need for funding to bring forward the market entry of this promising low-carbon steelmaking process.